Colon histology slide classification with deep-learning framework using individual and fused features
Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recomme...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023861?viewType=HTML |
| _version_ | 1797627790774763520 |
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| author | Venkatesan Rajinikanth Seifedine Kadry Ramya Mohan Arunmozhi Rama Muhammad Attique Khan Jungeun Kim |
| author_facet | Venkatesan Rajinikanth Seifedine Kadry Ramya Mohan Arunmozhi Rama Muhammad Attique Khan Jungeun Kim |
| author_sort | Venkatesan Rajinikanth |
| collection | DOAJ |
| description | Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively. |
| first_indexed | 2024-03-11T10:29:31Z |
| format | Article |
| id | doaj.art-3a77c3d9a75d4b25859aecfcc5f17cc1 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T10:29:31Z |
| publishDate | 2023-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-3a77c3d9a75d4b25859aecfcc5f17cc12023-11-15T01:20:34ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-10-012011194541946710.3934/mbe.2023861Colon histology slide classification with deep-learning framework using individual and fused featuresVenkatesan Rajinikanth0Seifedine Kadry1Ramya Mohan2Arunmozhi Rama3Muhammad Attique Khan4Jungeun Kim51. Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India2. Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway 3. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates 4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos 1401, Lebanon1. Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India1. Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India5. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon6. Department of Software, Kongju National University, Cheonan, 31080, KoreaCancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.https://www.aimspress.com/article/doi/10.3934/mbe.2023861?viewType=HTMLcolorectal cancerhistology slidefused featuresensemble featuresclassification |
| spellingShingle | Venkatesan Rajinikanth Seifedine Kadry Ramya Mohan Arunmozhi Rama Muhammad Attique Khan Jungeun Kim Colon histology slide classification with deep-learning framework using individual and fused features Mathematical Biosciences and Engineering colorectal cancer histology slide fused features ensemble features classification |
| title | Colon histology slide classification with deep-learning framework using individual and fused features |
| title_full | Colon histology slide classification with deep-learning framework using individual and fused features |
| title_fullStr | Colon histology slide classification with deep-learning framework using individual and fused features |
| title_full_unstemmed | Colon histology slide classification with deep-learning framework using individual and fused features |
| title_short | Colon histology slide classification with deep-learning framework using individual and fused features |
| title_sort | colon histology slide classification with deep learning framework using individual and fused features |
| topic | colorectal cancer histology slide fused features ensemble features classification |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023861?viewType=HTML |
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